For much of their fifty-year lifespan, analytics were “artisanal”—hand-crafted, slow, and expensive to create. The goal was typically to create a report or dashboard using descriptive statistics, although there were occasionally some predictive regression models too.
“We have many thousands of employees who are pretty good with data and work with it every day. But they spend 80% of their time pulling the data together, and they should be spending 80% analyzing it.
But there was another attribute of artisanal analytics that I have seldom mentioned until now: they were individually-oriented. Like an individual artist or craftsperson, the analytical artisan created analytics for him or herself, perhaps facilitated by a support person. At most a more senior decision-maker might see the results of the analysis—after all, this was the “decision support” period.
But now we are in a new era in which data, analytics, and AI are increasingly considered key organizational assets. They are typically created by teams for teams, and the many producing teams might serve consumer teams that are colleagues, senior executives, customers, suppliers, or an entire ecosystem. Today, for an analytics artisan to create an analytical program for him or herself is considered a waste of value and time. And the highly centralized approach, in which a corporate business intelligence group prepares data, dashboards, and analytics for thousands of users, can be even slower and more expensive…READ ON